1. Field of the Invention
The present invention relates to distributed source coding and compressed sensing methods and systems, and more particularly, to a new method and system referred to as distributed compressed sensing.
2. Brief Description of the Related Art
Distributed Source Coding
While the theory and practice of compression have been well developed for individual signals, many applications involve multiple signals, for which there has been less progress. As a motivating example, consider a sensor network, in which a potentially large number of distributed sensor nodes can be programmed to perform a variety of data acquisition tasks as well as to network themselves to communicate their results to a central collection point (see D. Estrin, D. Culler, K. Pister, and G. Sukhatme, “Connecting the physical world with pervasive networks,” IEEE Pervasive Computing, vol. 1, no. 1, pp. 59-69, 2002 and G. J. Pottie and W. J. Kaiser, “Wireless integrated network sensors,” Comm. ACM, vol. 43, no. 5, pp. 51-58, 2000). In many sensor networks, and in particular battery-powered ones, communication energy and bandwidth are scarce resources; both factors make the reduction of communication critical.
Fortunately, since the sensors presumably observe related phenomena, the ensemble of signals they acquire can be expected to possess some joint structure, or inter-signal correlation, in addition to the intra-signal correlation in each individual sensor's measurements. For example, imagine a microphone network recording a sound field at several points in space. The time-series acquired at a given sensor might have considerable intra-signal (temporal) correlation and might be sparsely represented in a local Fourier basis. In addition, the ensemble of time-series acquired at all sensors might have considerable inter-signal (spatial) correlation, since all microphones listen to the same sources. In such settings, distributed source coding that exploits both intra- and inter-signal correlations might allow the network to save on the communication costs involved in exporting the ensemble of signals to the collection point (see T. M. Cover and J. A. Thomas, “Elements of Information Theory”, Wiley, New York, 1991; D. Slepian and J. K. Wolf, “Noiseless coding of correlated information sources,” IEEE Trans. Inf. Theory, vol. 19, pp. 471-480, July 1973; S. Pradhan and K. Ramchandran, “Distributed source coding using syndromes (DISCUS): Design and construction,” IEEE Trans. Inf. Theory, vol. 49, pp. 626-643, March 2003; Z. Xiong, A. Liveris, and S. Cheng, “Distributed source coding for sensor networks,” IEEE Signal Processing Mag., vol. 21, pp. 80-94, September 2004 and J. Wolfowitz, Coding Theorems of Information Theory, Springer-Verlag, Berlin, 1978).
A number of distributed coding algorithms have been developed that involve collaboration amongst the sensors, including several based on predictive coding (see H. Luo and G. Pottie, “Routing explicit side information for data compression in wireless sensor networks,” in Int. Conf. on Distirbuted Computing in Sensor Systems (DCOSS), Marina Del Rey, Calif., June 2005; B. Krishnamachari, D. Estrin, and S. Wicker, “Modelling data-centric routing in wireless sensor networks,” USC Computer Engineering Technical Report CENG 02-14, 2002 and R. Cristescu, B. Beferull-Lozano, and M. Vetterli, “On network correlated data gathering,” in Proc. INFOCOM 2004., Hong Kong, March 2004), a distributed KLT (see M. Gastpar, P. L. Dragotti, and M. Vetterli, “The distributed Karhunen-Loeve transform,” IEEE Trans. Info Theory, November 2004, Submitted), and distributed wavelet transforms (see R. Wagner, V. Delouille, H. Choi, and R. G. Baraniuk, “Distributed wavelet transform for irregular sensor network grids,” in IEEE Statistical Signal Processing (SSP) Workshop, Bordeaux, France, July 2005 and A. Ciancio and A. Ortega, “A distributed wavelet compression algorithm for wireless multihop sensor networks using lifting,” in IEEE 2005 Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Philadelphia, March 2005). Three-dimensional wavelets have been proposed to exploit both inter- and intra-signal correlations (see D. Ganesan, B. Greenstein, D. Perelyubskiy, D. Estrin, and J. Heidemann, “An evaluation of multi-resolution storage for sensor networks,” in Proc. ACM SenSys Conference, Los Angeles, November 2003, pp. 89-102). Note, however, that any collaboration involves some amount of inter-sensor communication overhead.
In the Slepian-Wolf framework for lossless distributed coding (see T. M. Cover and J. A. Thomas, “Elements of Information Theory”, Wiley, New York, 1991; D. Slepian and J. K. Wolf, “Noiseless coding of correlated information sources,” IEEE Trans. Inf. Theory, vol. 19, pp. 471-480, July 1973; S. Pradhan and K. Ramchandran, “Distributed source coding using syndromes (DISCUS): Design and construction,” IEEE Trans. Inf. Theory, vol. 49, pp. 626-643, March 2003; Z. Xiong, A. Liveris, and S. Cheng, “Distributed source coding for sensor networks,” IEEE Signal Processing Mag., vol. 21, pp. 80-94, September 2004 and J. Wolfowitz, Coding Theorems of Information Theory, Springer-Verlag, Berlin, 1978), the availability of correlated side information at the collection point/decoder enables each sensor node to communicate losslessly at its conditional entropy rate rather than at its individual entropy rate. Slepian-Wolf coding has the distinct advantage that the sensors need not collaborate while encoding their measurements, which saves valuable communication overhead. Unfortunately, however, most existing coding algorithms (see S. Pradhan and K. Ramchandran, “Distributed source coding using syndromes (DISCUS): Design and construction,” IEEE Trans. Inf. Theory, vol. 49, pp. 626-643, March 2003 and Z. Xiong, A. Liveris, and S. Cheng, “Distributed source coding for sensor networks,” IEEE Signal Processing Mag., vol. 21, pp. 80-94, September 2004) exploit only inter-signal correlations and not intra-signal correlations. To date there has been only limited progress on distributed coding of so-called “sources with memory.” (We briefly mention some limitations here and elaborate in the section on challenges for distributed coding of sources with memory.) The direct implementation for such sources would require huge lookup tables (see T. M. Cover and J. A. Thomas, “Elements of Information Theory”, Wiley, New York, 1991 and T. M. Cover, “A proof of the data compression theorem of Slepian and Wolf for ergodic sources,” IEEE Trans. Inf. Theory, vol. 21, pp. 226-228, March 1975). Furthermore, approaches combining pre- or post-processing of the data to remove intra-signal correlations combined with Slepian-Wolf coding for the inter-signal correlations appear to have limited applicability. Finally, a recent paper by Uyematsu (see T. Uyematsu, “Universal coding for correlated sources with memory,” in Canadian Workshop Inf. Theory, Vancouver, June 2001) provides compression of spatially correlated sources with memory. However, the solution is specific to lossless distributed compression and cannot be readily extended to lossy compression setups. We conclude that the design of constructive techniques for distributed coding of sources with both intra- and inter-signal correlation is still an open and challenging problem with many potential applications.
Challenges for Distributed Coding of Sources with Memory
One approach to distributed compression of data with both inter- and intra-signal correlations (“sources with memory”) is to perform Slepian-Wolf coding using source models with temporal memory. Cover (see T. M. Cover, “A proof of the data compression theorem of Slepian and Wolf for ergodic sources,” IEEE Trans. Inf. Theory, vol. 21, pp. 226-228, March 1975) showed how random binning can be applied to compress ergodic sources in a distributed manner. Unfortunately, implementing this approach would be challenging, since it requires maintaining lookup tables of size 2NR1 and 2NR2 at the two encoders. Practical Slepian-Wolf encoders are based on dualities to channel coding (see S. Pradhan and K. Ramchandran, “Distributed source coding using syndromes (DISCUS): Design and construction,” IEEE Trans. Inf. Theory, vol. 49, pp. 626-643, March 2003 and Z. Xiong, A. Liveris, and S. Cheng, “Distributed source coding for sensor networks,” IEEE Signal Processing Mag., vol. 21, pp. 80-94, September 2004) and
An alternative approach would use a transform to remove intra-signal correlations. For example, the Burrows-Wheeler Transform (BWT) permutes the symbols of a block in a manner that removes correlation between temporal symbols and thus can be viewed as the analogue of the Karhunen-Lòeve transform for sequences over finite alphabets. The BWT handles temporal correlation efficiently in single-source lossless coding (see D. Baron and Y. Bresler, “An O(N) semi-predictive universal encoder via the BWT,” IEEE Trans. Inf. Theory, vol. 50, no. 5, pp. 928-937, 2004 and M. Effros, K. Visweswariah, S. R. Kulkarni, and S. Verdu, “Universal lossless source coding with the Burrows Wheeler transform,” IEEE Trans. Inf. Theory, vol. 48, no. 5, pp. 1061-1081, 2002). For distributed coding, the BWT could be proposed to remove temporal correlations by pre-processing the sequences prior to Slepian-Wolf coding. Unfortunately, the BWT is input-dependent, and hence temporal correlations would be removed only if all sequences were available at the encoders. Using a transform as a post-processor following Slepian-Wolf coding does not seem promising either, since the distributed encoders' outputs will each be independent and identically distributed.
In short, approaches based on separating source coding into two components—distributed coding to handle inter-signal correlations and a transform to handle intra-signal correlations—appear to have limited applicability. In contrast, a recent paper by Uyematsu (see T. Uyematsu, “Universal coding for correlated sources with memory,” in Canadian Workshop Inf. Theory, Vancouver, June 2001) proposed a universal Slepian-Wolf scheme for correlated Markov sources. Uyematsu's approach constructs a sequence of universal codes such that the probability of decoding error vanishes when the coding rates lie within the Slepian-Wolf region. Such codes can be constructed algebraically, and the encoding/decoding complexity is O(N3). While some of the decoding schemes developed below have similar (or lower) complexity, they have broader applicability. First, we deal with continuous sources, whereas Uyematsu's work considers only finite alphabet sources. Second, quantization of the measurements will enable us to extend our schemes to lossy distributed compression, whereas Uyematsu's work is confined to lossless settings. Third, Uyematsu's work only considers Markov sources. In contrast, the use of different bases enables our approaches to process broader classes of jointly sparse signals.
Compressed Sensing (CS)
A new framework for single-signal sensing and compression has developed recently under the rubric of Compressed Sensing (CS). CS builds on the ground-breaking work of Candès, Romberg, and Tao (see E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, 2006) and Donoho (see D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006), who showed that if a signal has a sparse representation in one basis then it can be recovered from a small number of projections onto a second basis that is incoherent with the first. Roughly speaking, incoherence means that no element of one basis has a sparse representation in terms of the other basis. This notion has a variety of formalizations in the CS literature (see E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, 2006; D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006; E. Candès and T. Tao, “Near optimal signal recovery from random projections and universal encoding strategies,” August 2004, Preprint and J. Tropp and A. C. Gilbert, “Signal recovery from partial information via orthogonal matching pursuit,” April 2005, Preprint).
In fact, for an N-sample signal that is K-sparse, only K+1 projections of the signal onto the incoherent basis are required to reconstruct the signal with high probability (Theorem 1). By K-sparse, we mean that the signal can be written as a sum of K basis functions from some known basis. Unfortunately, this requires a combinatorial search, which is prohibitively complex. Candès et al. (see E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, 2006) and Donoho (see D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006) have recently proposed tractable recovery procedures based on linear programming, demonstrating the remarkable property that such procedures provide the same result as the combinatorial search as long as cK projections are used to reconstruct the signal (typically c≈3 or 4) (see E. Candès and T. Tao, “Error correction via linear programming,” Found. of Comp. Math., 2005, Submitted; D. Donoho and J. Tanner, “Neighborliness of randomly projected simplices in high dimensions,” March 2005, Preprint and D. Donoho, “High-dimensional centrally symmetric polytopes with neighborliness proportional to dimension,” January 2005, Preprint). Iterative greedy algorithms have also been proposed (see J. Tropp, A. C. Gilbert, and M. J. Strauss, “Simulataneous sparse approximation via greedy pursuit,” in IEEE 2005 Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Philadelphia, March 2005; M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, “Fast reconstruction of piecewise smooth signals from random projections,” in Online Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS), Rennes, France, November 2005 and C. La and M. N. Do, “Signal reconstruction using sparse tree representation,” in Proc. Wavelets XI at SPIE Optics and Photonics, San Diego, August 2005), allowing even faster reconstruction at the expense of slightly more measurements.
Yet despite these significant revelations, there has been little work on distributed source coding in the CS community. We now survey such related work.
Related Work on Distributed Source Coding with CS
Recently, Bajwa, Rabbat, Haupt, Sayeed and Nowak (see W. Bajwa, J. Haupt, A. Sayeed and R. Nowak, “Compressive Wireless Sensing” , in Proc. Inf. Processing in Sensor Networks (IPSN), Nashville, Tenn., April 2006 and M. Rabbat, J. Haupt and R. Nowak, “Decentralized Compression and Predistribution via Randomized Gossiping”, in Proc. Inf. Processing in Sensor Networks (IPSN), Nashville, Tenn., April 2006) formulated settings for CS in sensor networks that exploit inter-signal correlations. In their approaches, each sensor nε{1, 2, . . . , N} simultaneously records a single reading x(n) of some spatial field (temperature at a certain time, for example). Note that in this section only, N refers to the number of sensors and not the length of the signals. Each of the sensors generates a pseudorandom sequence rn(m), m=1, 2, . . . , M, and modulates the reading as x(n)rn(m). In the first scheme, each sensor n then transmits its M numbers in sequence in an analog and synchronized fashion to the collection point such that it automatically aggregates them, obtaining M measurements y(m)=Σn=1Nx(n)rn(m). In the second scheme, sensors select other sensors at random to communicate their own modulated values, to be averaged, so that as more communications occur each sensor will asymptotically obtain the value of the measurement y(m); thus, every sensor becomes a collection point. Thus, defining x=[x(1), x(2), . . . , x(N)]T and φm=[r1(m), r2(m), . . . , rN(m)], the collection points automatically receive the measurement vector y=[y(1), y(2), . . . , y(M)]T after O(M) transmission steps. The samples x(n) of the spatial field can then be recovered using CS provided that x has a sparse representation in a known basis. The coherent analog transmission in the first scheme also provides a power amplification property, thus reducing the power cost for the data transmission by a factor of N. There are significant shortcomings to these approaches, however. Sparse representations for x are straightforward when the spatial samples are arranged in a grid, but establishing such a representation becomes much more difficult when the spatial sampling is irregular (see R. Wagner, V. Delouille, H. Choi, and R. G. Baraniuk, “Distributed wavelet transform for irregular sensor network grids,” in IEEE Statistical Signal Processing (SSP) Workshop, Bordeaux, France, July 2005). Additionally, since this method operates at a single time instant, it exploits only inter-signal and not intra-signal correlations; that is, it essentially assumes that the sensor field is i.i.d. from time instant to time instant. In contrast, we will develop signal models and algorithms that are agnostic to the spatial sampling structure and that exploit both inter- and intra-signal correlations.